System and method of managing a driver take-over from an autonomous vehicle based on monitored driver behavior

ABSTRACT

A method of managing operator take-over of autonomous vehicle. The method includes gathering information on an external surrounding of the autonomous vehicle; analyzing the gathered information on the external surrounding of the autonomous vehicle to determine an upcoming traffic pattern; gathering information on an operator of the autonomous vehicle; analyzing the gathered information on the operator of the autonomous vehicle to determine an operator behavior; predicting an operator action based on the determined upcoming traffic pattern and the determined operator behavior; and initiating a predetermined vehicle response based on the predicted operator action. The predicting the operator action includes comparing the determined upcoming traffic pattern with a similar historic traffic pattern and retrieving a historical operator action in response to the similar historical pattern.

INTRODUCTION

The present disclosure relates to Advanced Driver Assistance Systemequipped vehicles, more specifically to a system and method of managinga driver take-over from the Advanced Driver Assistance System based onmonitored behavior of the vehicle operator.

Advanced Driver Assistance Systems (ADAS) are intelligent systems thatreside onboard a vehicle and assist the driver, also is referred to asthe vehicle operator, in the operation of the vehicle. ADAS are used toenhance or automate selective motor vehicle systems in order to increasethe vehicle operator's driving performance or increase the levels ofautonomous driving in accordance with SAE J3016 levels of DrivingAutomation. A typical ADAS includes an ADAS module that is incommunication with various vehicle exterior sensors, vehicle statesensors, and selective vehicle control systems such as steering,acceleration, and braking systems. The ADAS module analyzes informationgathered by the exterior sensors and vehicle state sensors to generateand communicates instructions to the vehicle control systems for partialor full autonomous control of the vehicle. The ADAS may also include aDriver Monitoring System (DMS) having a DMS module that is incommunications with various vehicle interior sensors configured tomonitor the behavior of the vehicle operator, such as eye glances,facial expressions, body movements, and other subject related factors topredict the fatigue, distraction, and emotional state of the vehicleoperator.

In one operating scenario, when the ADAS is operating in a lower-levelautonomous mode (i.e. SAE J3016 levels 0-2) and the DMS detects thevehicle operator is potentially fatigued or distracted, the DMS mayactivate an audible or visual alert to warn the vehicle operator and/orcommunicate with the ADAS module to take over the control of the vehiclefrom the vehicle operator. In another operating scenario, when the ADASis operating in a higher-level autonomous mode (i.e. SAE J3016 level3-5) and the ADAS module encounters a driving scenario that mightrequire manual control of the vehicle, the ADAS may instruct the DMS toactivate an audible or visual alert to request the vehicle operator totake manual control of the vehicle. In yet another operating scenario,when the ADAS is operating in partial to full autonomous mode and thevehicle operator has insufficient confidence that the ADAS is capable ofadequately negotiating a traffic situation, the vehicle operator mayvoluntary take-over control of the ADAS.

The vehicle operator taking-over control of the ADAS is referred to astaking-over control of the autonomous vehicle, or simply as take-over.The ADAS requesting the vehicle operator to take manual control of thevehicle is referred to as handing-over control of the autonomousvehicle, or simply as hand-over.

Thus, while ADAS equipped vehicles having DMS achieve their intendedpurpose, there is a need for continuous improvement to enhance thequality of experience of the vehicle operator by reducing the perceivedneed or desire for the vehicle operator to take-over control from theADAS and by reducing the frequency of hand-over requests for the vehicleoperator to take-over control from the ADAS.

SUMMARY

A method of managing operator take-over of autonomous vehicle. Themethod includes gathering information on an external surrounding of theautonomous vehicle; analyzing the gathered information on the externalsurrounding of the autonomous vehicle to determine an upcoming trafficpattern; gathering information on an operator of the autonomous vehicle;analyzing the gathered information on the operator of the autonomousvehicle to determine an operator behavior; predicting an operator actionbased on the determined upcoming traffic pattern and the determinedoperator behavior; and initiating a predetermined vehicle response basedon the predicted operator action. The predicting of the operator actionincludes comparing the determined upcoming traffic pattern with asimilar historic traffic pattern and retrieving a historical operatoraction in response to the similar historical pattern.

A method of managing a vehicle operator's intent, due to perceived needor desire, to take-over control of an autonomous vehicle is disclosed.The method includes gathering, by at least one exterior sensor,information on an upcoming traffic pattern; gathering, by at least oneinterior sensor, information on a behavior of the vehicle operator;analyzing the behavior of the vehicle operator in response to theupcoming traffic pattern to determine when the vehicle operator has aperceived need to take-over control of the autonomous vehicle; andinitiating a change in a dynamic of the autonomous vehicle to eliminatethe perceived need of the vehicle operator to take-over control of theautonomous vehicle.

A method of managing a warning priority to an operator of a vehicle. Themethod includes gathering exterior information on a surrounding aboutthe vehicle; gathering interior information on the operator of thevehicle; analyzing the exterior information to determine an upcomingtraffic pattern; analyzing the interior information to determine anoperator behavior in response to the upcoming traffic pattern;predicting an operator action based on the determined operator behaviorin a response to the determined upcoming traffic pattern; andprioritizing a warning based on the predicted operator action.

Further areas of applicability will become apparent from the descriptionprovided herein. It should be understood that the description andspecific examples are intended for purposes of illustration only and arenot intended to limit the scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are for illustration purposes only and arenot intended to limit the scope of the present disclosure in any way.

FIG. 1 is a functional diagram of an autonomous vehicle equipped with anAdvanced Driver Assistance System (ADAS) having a Driver Monitory System(DMS), according to an exemplary embodiment;

FIG. 2 is a block diagram of a method of managing a driver take-over ofthe autonomous vehicle of FIG. 1 , according to an exemplary embodiment;

FIG. 3 is a function diagram of an autonomous vehicle system, accordingto an exemplary embodiment;

FIG. 4 is a plan view of a traffic pattern that may induce a perceivedneed or desire for the vehicle operator to take-over control of theautonomous vehicle, according to an exemplary embodiment;

FIG. 5 is a flow block diagram showing a method of managing theperceived need or desire for taking-over the control of the autonomousvehicle, according to an exemplary embodiment;

FIG. 6 shows a plan view of a traffic pattern that may induce the ADASto issue a warning priority for the vehicle operator to take-overcontrol of the autonomous vehicle, according to an exemplary embodiment;

FIG. 7 is a flow block diagram of a method 700 of changing a warningpriority to the vehicle operator according to an exemplary embodiment;and

FIG. 8 is a plan view of a traffic pattern where a driver glancebehavior may be utilized to predict take-take, according to an exemplaryembodiment;

DETAILED DESCRIPTION

The following description is merely exemplary in nature and is notintended to limit the present disclosure, application, or uses. Theillustrated embodiments are disclosed with reference to the drawings,wherein like numerals indicate corresponding parts throughout theseveral drawings. The figures are not necessarily to scale and somefeatures may be exaggerated or minimized to show details of particularfeatures. The specific structural and functional details disclosed arenot intended to be interpreted as limiting, but as a representativebasis for teaching one skilled in the art as to how to practice thedisclosed concepts.

As used herein, a module or control module means any one or variouscombinations of one or more processors, associated memory, and othercomponents operable to execute a software, firmware, program,instruction, routine, code, and algorithm to provide the describedfunctions. Processors include, but not limited to, Application SpecificIntegrated Circuits (ASIC), electronic circuits, central processingunits, microprocessors, and microcontrollers. Associated memoryincludes, but not limited to, read only memory (ROM), random accessmemory (RAM), and electrically programmable read only memory (EPROM).Functions of a control module as set forth in this disclosure may beperformed in a distributed control architecture among several networkedcontrol modules. A control module may include a variety of communicationinterfaces including point-to-point or discrete lines and wired orwireless interfaces to other control modules.

Software, firmware, programs, instructions, routines, code, algorithmsand similar terms mean any control module executable instruction setsincluding methods, calibrations, data structures, and look-up tables. Acontrol module has a set of control routines executed to providedescribed functions. Routines are executed, such as by a centralprocessing unit, and are operable to monitor inputs from sensing devicesand other networked control modules and execute control and diagnosticroutines to control operation of actuators. Routines may be executed atregular intervals during ongoing vehicle operation. Alternatively,routines may be executed in response to occurrence of an event, softwarecalls, or on demand via user interface inputs or requests.

FIG. 1 shows a functional diagram of an exemplary vehicle 100 equippedwith an Advanced Driver Assistance System (ADAS) 102 having a DriverMonitoring System (DMS) 104. The ADAS 102 is configured to provide alevel of driving automation from partial autonomous mode to fullautonomous mode in accordance with SAE J3016 Levels of DrivingAutomation. Lower levels of driving automation can include a range ofdynamic driving and vehicle operation including some level of automaticcontrol or intervention related to simultaneous automatic control ofmultiple vehicle functions, such as steering, acceleration, and braking,wherein the operator retains partial control of the vehicle. Higherlevels of driving automation can also include full automatic control ofall vehicle driving functions, including steering, acceleration,braking, and executing maneuvers such as automated lane changes, whereinthe driver cedes most or all control of the vehicle for a period oftime. The vehicle 102 is also referred to as an autonomous vehicle 102.

The ADAS 102 includes an ADAS module 106, also referred to as an ADAScontrol module 106, configured to communicate with various systems ofvehicle 100, such as a detection system 128, acceleration system 130,steering system 132, navigation system 136, positioning system 138,deceleration system 140, and other systems necessary for partially orfully control the movements, speed, direction, etc. of the vehicle 102.The DMS 104 includes an DMS module 108, also referred to as an DMScontrol module 108, configured to communicate with the ADAS module 106and to receive data from at least one internal sensor 150 configured tomonitor the vehicle operator (not shown). These vehicle systems 128,130, 132, 136, 138, 140 may have system specific control modules (notshown) in communications with the ADAS modules 106 for the coordinatedcontrol of the vehicle 102. In an alternative embodiment, the ADASmodule 106 may function as a main control module for directlycontrolling all or working in combinations with the system specificcontrol modules to control one or more of the systems 128, 130, 132,136, 138, 140.

The detection system 128 is in communications with the exterior sensors152 including, but not limited to, optical laser devices such as a LightDetection and Ranging (LIDAR) device 152A for having 360 degrees of viewabout the host vehicle 102, a forward viewing camera 152B, a rearwardviewing camera 152C, sideview cameras 152D and range sensors 152E suchas radar and sonar devices. The detection system 128 is incommunications with the interior sensors 150 including, but not limitedto, a camera. Each of these interior sensors 150 and exterior sensors152 may be equipped with localized processing components which processgathered data and provide processed or raw sensor data directly to oneor more of the detection system 128, ADAS module 106, and DMS module108.

The vehicle 102 may also include a communication system 142 having acircuit configured with Dedicated Short-Range Communications protocol(e.g. WiFi) for communication with other vehicles equipped with similarcommunication systems. The communication system may be configured forvehicle-to-vehicle communications (V2V), vehicle-to-infrastructure(V2I), and vehicle-to-everything (V2X) communications.

Communications between the ADAS 102, DMS 104, vehicle systems 128, 130,132, 136, 138, 140, 142, interior sensors 150, and exterior sensors 152,may be implemented by using a direct wired point-to-point link, anetworked communication bus link, a wireless link or another suitablecommunication link 170. Communication includes exchanging data signalsin suitable form, including, for example, electrical signals viaconductive medium, electromagnetic signals via air, optical signals viaoptical waveguides, and the like. The data signals may include discrete,analog, or digitized analog signals representing inputs from sensors,actuator commands, and communication between vehicle systems andmodules.

FIG. 2 shows a functional block diagram for a method 200 of managingdriver take-over of the autonomous vehicle 102. Method 200 enhances thequality of experience of an operator of the autonomous vehicle by (i)reducing or eliminating the perceived need or desire for take-overactions by the operator and (ii) reducing or eliminating the frequencyor number of generated alerts, warnings, or notifications for theoperator to initiate a take-over action.

The operator of the autonomous vehicle is also referred to as operatorof vehicle, vehicle operator, operator, or simply as driver. A take-overaction, or take-over, is defined as the vehicle operator initiating anaction to take-over a function of the ADAS, also referred to astake-over of the autonomous vehicle. Examples of a take-over actionincludes, but not limited to, the vehicle operator taking-overoperational control of the vehicle from the ADAS by inputting a commandonto a steering device, depressing the accelerator pedal, and/or bydepressing a brake pedal. The vehicle operator's intent or motivation toinitiate a take-over may be due to a perceive need or a desire by thevehicle operator to take over control.

In block 202, the exterior sensors 152 gather information on an externalsurrounding of the vehicle 102. In block 204, the communication system142 may receive information wirelessly on the external surrounding ofthe vehicle 102 from roadside units or other vehicles equipped with V2Vor V2X communications. Information gathered from the exterior sensors152 and wireless communications may include surrounding vehicle layout,vehicle dynamics, road geometry, weather, lightening condition, andother necessary information for the ADAS module to perceive andnegotiate through an upcoming traffic pattern. An example of an upcomingtraffic pattern includes, but not limited to, a layout or geometry of aroad in the path of the autonomous vehicle, vehicles traveling in theroad, objects in the road that the autonomous vehicle will need tonegotiate through or around, and environmental context such as weatherand lighting conditions.

Moving to block 206 from block 202 and block 204, the informationcollected by the exterior sensors 152 and V2X communications areanalyzed to determine an upcoming traffic pattern.

In block 208, the interior sensors 150 gather information on theoperator of the autonomous vehicle 102. The information collected by theinterior sensors 150 are analyzed to determine the behavior of theoperator. The operator's facial expressions, eye glances, body gesturesincluding posture, and other subject related factors are analyzed inblocks 210, 212, 214, and 216, respectively. Subject related factorsinclude fatigue, situation awareness, trust, and the likes.

Moving to block 222, the DMS predicts an operator action based on thedetermined operator behavior and the determined upcoming trafficpattern. The DMS module 108 executes a prediction model by retrievinghistorical data from block 218. The historical data including aplurality of historical traffic patterns and a plurality of historicaloperator behavior and resulting actions corresponding to the pluralityof historical traffic patterns. The DMS module 108 compares thedetermined upcoming traffic pattern with a similar historical trafficpattern and retrieves a historical operator action corresponding to thesimilar historical pattern. The DMS module 108 then predicts a tendencyand probability of take-over action by the operator by comparing thedetermined upcoming traffic pattern and observed behavior of theoperator with the historical traffic pattern and historical operatorbehavior.

Each vehicle operator has their own personalized prediction model basedon their specific historical data. Each new determined upcoming trafficpattern and corresponding determined operator behavior may be added tothe historical data. Referring back to block 222, an optimizationalgorithm may be utilized to predict more accurately the tendency andprobability of operator take-over based on new and historical trafficpatterns and operator's behaviors from block 218 in response to thesenew and historical traffic patterns. The optimization algorithm may bestored in and executed by the DMS module 108.

An example operator behavior that may be used to predict operatortake-over and change vehicle dynamics may be that of the operator'sglance behavior. The operator's eye glances may be analyzed to determinearea of interest, fixation duration, saccade amplitude, etc.

In block 220, the external information gathered in block 202 and block204 is communicated to the ADAS module. The predicted tendency oftake-over/no take-over and probability of take-over action by theoperator from block 222 are also communicated to the ADAS module inblock 220. The ADAS module communicates with the vehicle system modules300 to execute a change in vehicle dynamics or vehicle maneuvers toeliminate a perceived need for the take-over action or pre-empt an alertto the operator for taking over.

Referring to FIG. 3 , from block 220, the ADAS module may communicatewith steering control module 302 for controlling the EPS motor; theacceleration control module 306 for controlling the engine controlmodule (ECM) 308, torque control module (TCM) 310, and power invertedmodule (TCM) 312; and electronic brake control module (EBCM) 316 forcontrolling the brakes 318. In block 320, the priority of escalation ofsystem activations may be predetermined based on the severity of theupcoming traffic pattern and observed operator behavior.

Example 1—Modifying Vehicle Dynamics

FIG. 4 shows a plan view 400 of a traffic pattern that may induce aperceived need by the vehicle operator to take-over control of theautonomous vehicle. FIG. 5 shows a block diagram showing a method 500 ofmanaging the vehicle operator's perceived need to take-over theautonomous vehicle by modifying the autonomous vehicle's dynamics.

Referring to FIG. 4 , the plan view 400 shows a two lane road 401divided into first lane 402 and a second lane 404 by a longitudinaldashed line 406. A third lane 408 is shown merging with the first lane402. An autonomous vehicle 410 is shown traveling in the first lane 402at a time stamp 1 (T1) before the third lane 408 intersects the firstlane 402. A target vehicle 412 is shown in the third lane 408 travelingtoward the first lane 402 at the time stamp 1 (T1).

Referring to both FIG. 4 and FIG. 5 , the method 500 starts in block 502when the DMS analyses the information gathered by the exterior sensors152 and determines the upcoming traffic pattern to be an upcomingtraffic pattern as shown in FIG. 4 with a target vehicle 412 merginginto a first lane 402 in which the autonomous vehicle 410 is traveling.

Moving to block 504, the DMS analyzes the information gathered by theinterior sensor and determines a behavior of the vehicle operator inresponse to the upcoming traffic pattern of FIG. 4 . The behavior may beinterpreted as the vehicle operator having low confidence of the ADAS tonegotiate such a traffic pattern or the vehicle operator is concern witha potential collision with the target vehicle 412. The behavior of thevehicle operator may be determined based on the eye glances, facialexpressions, body gestures, and other relevant biometric factorsexhibited by the vehicle operator.

Moving to block 506, the DMS predicts a potential take-over by thevehicle operator based on historical data on the historical behavior ofvehicle operator when in a similar historical traffic pattern as shownin FIG. 4 . The DMS predicts a probability of the vehicle operatortaking-over of the autonomous vehicle to avoid the merging targetvehicle 412. The DMS may also communicate with the ADAS to determine aprobability of collision of the merging target vehicle 412.

Moving to block 508, if the probability of the vehicle operatortaking-over control of the autonomous vehicle is above a predeterminedtake-over level OR if the probability of collision with the mergingtarge vehicle 412 is above a predetermined collision level, then method500 proceeds to block 510. The term “OR” is defined as an “inclusive or”meaning either this, or that, or both. In block 510, the DMScommunicates with the ADAS to modify the dynamics of the autonomousvehicle 410 in block 510 diverting the autonomous vehicle to the secondlane 404 (e.g. changing lanes) at a position indicated by a time stamp 2(T2), which is later then time stamp 1 (T1), as the merging targetvehicle 412 approaches a position indicated at the time stamp 2 (T2).

Moving to block 512 from block 510, if the vehicle operator initiates atake-over, it means the change in vehicle dynamic from block 510 tomitigate the collision is not the desired way for the vehicle operator.The data point is recorded in historical data in block 504. If thevehicle operator does not initiate a take-over, then the method proceedsto block 514 and ends.

Referring back to block 508, if the probability of the vehicle operatortaking-over control of the autonomous vehicle is at or below thepredetermined take-over level AND the probability of collision with themerging targe vehicle 412 is at or below the predetermined collisionlevel, then the DMS does not intervene to manage the operator take-overof the autonomous vehicle and ends at block 514, then the methodproceeds to block 514 and ends.

Example 2—Changing the Warning Priority

FIG. 6 shows a plan view 600 of a traffic pattern that may induce theADAS to issue a warning or alert for the vehicle operator to take-overcontrol of the autonomous vehicle. FIG. 7 shows a block diagram of amethod 700 of changing a warning priority to the vehicle operator.

Referring to FIG. 6 , the plan view 600 shows a road 602 having a firstlane 604 and a opposite direction second lane 606 separated by a dashline 608. A first island 610 is disposed in the road 602 separating thetwo opposite lanes 604, 606 to define a first round-about 612 and asecond island 614 is disposed further down the road 602 separating thetwo opposite lanes 604, 606 to define a second round-about 616. Anautonomous vehicle 618 is shown traveling in the first lane 604 enteringthe first round-about 612 at time stamp 1 (T1) and entering the secondround-about at a later time stamp 2 (T2).

Referring to both FIG. 6 and FIG. 7 , the method 700 starts in block 702when the DMS analyses the information gathered by the exterior sensors152 and determines the upcoming traffic pattern to be a firstround-about potentially followed by a second round-about.

Moving to block 704, the DMS analyzes the information gathered by theinterior sensor(s) and determines a behavior of the vehicle operator inresponse to the upcoming traffic pattern. The behavior of the vehicleoperator may be determined based on the eye glances, facial expressions,body gestures, and other relevant biometric factors exhibited by thevehicle operator.

Moving to block 706, the DMS searches the historical data base todetermine if the vehicle operator has experienced a similar trafficpattern as shown in FIG. 6 . If the historical data has not shown thatthe vehicle operator has experienced a similar traffic pattern as shownin FIG. 6 , then the method 700 moves to block 714 and ends. If thehistorical data does show that the vehicle operator has experienced asimilar traffic pattern as shown in FIG. 6 , then the method 700continues to block 708.

Moving to block 708, the DMS analyzes the information gathered by theinterior sensors 150 to determine a probability that the vehicle isabout to take-over the autonomous vehicle. If the determined probabilityis at or below a predetermined level, then the method moves to block 714and ends.

Referring back to block 708, if the determined probability is above thepredetermined level, then the DMS communicates with the ADAS to cancelthe pending hand-over escalation, for example, by not issuing a driveralert. Moving to block 712, if the vehicle operator does not take-overthe autonomous vehicle, the method moves back to block 704 andcontinues. The failure of hand-over also means that the prediction wasinaccurate. Then the data point is recorded in the historical data inblock 704 for future alignment. Referring back to 712, if the vehicleoperator does take-over the autonomous vehicle, then the method moves toblock 714 and ends.

Example 3—Using Glance Behavior to Predict Take-over

FIG. 8 shows a plan view 800 of a traffic pattern where a glancebehavior of the vehicle operator may be utilized to predict a take-overaction and to change the vehicle behavior to preempt such a take-overaction. The plan view 800 shows a two lane road 801 divided into firstlane 802 and a second lane 804 by a longitudinal dashed line 806. Anautonomous vehicle 810 is shown traveling in the first lane 802, a firsttarget vehicle 812 is shown in the first lane 802 ahead of theautonomous vehicle 810, and a second target vehicle 814 is shown in thesecond lane 804 adjacent the autonomous vehicle 810.

The autonomous vehicle 810 is shown approaching the first target vehicle812 at a longitudinal closing speed (V_(Lo)) at a longitudinal closingdistance (D_(Lo)). When V_(Lo) exceeds a predetermined longitudinalclosing speed and/or D_(Lo) is less than a predetermined longitudinalclosing distance, AND a glance of the vehicle operator is fixed on apredetermined target for greater than a predetermined time limit, thenthe ADAS may adjust the speed of the autonomous vehicle 810 to increasethe relative closing distance between the autonomous vehicle 810 and thefirst target vehicle 812 to preempt a take-over action by the driver.For example, the predetermined longitudinal closing speed may be 5miles/hour or greater, the predetermined longitudinal closing distancemay be 29 meters or less, the predetermined glance time limit may be 2seconds or greater, and the predetermined glance target may be the firsttarget vehicle 812. Alternatively, a predetermined operator glancepattern may be used to predict a driver take-over.

The second target vehicle 814 is shown approaching the autonomousvehicle 810 at a lateral closing speed (V_(La)) at a lateral closingdistance (D_(La)). When Via exceeds a predetermined lateral closingspeed and/or D_(La) is less than a predetermined lateral closingdistance, AND a glance of the vehicle operator is fixed on apredetermined target for greater than a predetermined time limit, thenthe ADAS may increase the lateral closing distance or increase thelateral overlap between the vehicles to preempt a take-over action bythe driver. For example, the predetermined lateral closing speed may be1 mile/hour or greater, the predetermined lateral closing distance maybe 0.7 meters or less, the predetermined glance time limit may be 1second or greater, and the predetermined glance target may be the secondtarget vehicle 814. Alternatively, a predetermined operator glancepattern may be used to predict a driver take-over.

The above disclosed systems and methods provide an enhanced quality ofexperience of the vehicle operator by reducing the perceived need ordesire for the vehicle operator to take-over control from the ADAS andby reducing the frequency of hand-over requests for the vehicle operatorto take-over control from the autonomous vehicle.

The description of the present disclosure is merely exemplary in natureand variations that do not depart from the general sense of the presentdisclosure are intended to be within the scope of the presentdisclosure. Such variations are not to be regarded as a departure fromthe spirit and scope of the present disclosure.

1. A method of managing an operator take-over of an autonomous vehicle,comprising: gathering, by at least one exterior sensor, information onan exterior surrounding of the autonomous vehicle; analyzing thegathered information on the exterior surrounding of the autonomousvehicle to determine an upcoming traffic pattern; gathering, by at leastone interior sensor, information on an operator of the autonomousvehicle; analyzing the gathered information on the operator of theautonomous vehicle to determine an operator behavior in response to theupcoming traffic pattern; predicting an operator action based on thedetermined operator behavior in response to the upcoming trafficpattern; and initiating a predetermined vehicle response based on thepredicted operator action.
 2. The method of claim 1, wherein predictingthe operator action comprises: searching a data base containing aplurality of historical traffic patterns and a plurality of historicaloperator behavior and resulting actions in response to the plurality ofhistorical traffic patterns.
 3. The method of claim 2, whereinpredicting the operator action further comprises: comparing thedetermined operator behavior in response to the upcoming traffic patternto a similar historical traffic pattern and a historical operatorbehavior and resulting action in response to the similar historicaltraffic pattern.
 4. The method of claim 1, wherein the predeterminedvehicle response includes a vehicle maneuver that eliminates a perceivedneed for the predicted operator action.
 5. The method of claim 1,wherein the predetermined vehicle response includes eliminating ahand-over alert.
 6. The method of claim 1, wherein analyzing thegathered information on the operator of the autonomous vehicle todetermine an operator behavior in response to the upcoming trafficpattern includes analyzing at least one of a glance, a facialexpression, a body movement, and a posture of the operator.
 7. Themethod of claim 1, wherein the operator action is a take-over action. 8.The method of claim 1, wherein the information on an exteriorsurrounding of the autonomous vehicle and the information on theoperator of the autonomous vehicle are simultaneously gathered andanalyzed.
 9. The method of claim 1, wherein gathering information on anexterior surrounding of the autonomous vehicle includes receivingexterior information wirelessly by vehicle-to-everything (V2X)communications.
 10. The method of claim 1, wherein the predeterminedvehicle response includes one of: (i) initiating a vehicle maneuver toeliminate a take-over action and (ii) eliminating a hand-over alert tothe operator.
 11. A method of managing a vehicle operator's intent totake-over control of an autonomous vehicle, comprising: gathering, by atleast one exterior sensor, information on an upcoming traffic pattern;gathering, by at least one interior sensor, information on a behavior ofthe vehicle operator; analyzing the behavior of the vehicle operator inresponse to the upcoming traffic pattern to determine when the vehicleoperator has an intent to take-over control of the autonomous vehicle;and initiating a change in a dynamic of the autonomous vehicle toeliminate the intent of the vehicle operator to take-over control of theautonomous vehicle.
 12. The method of claim 11, wherein the exteriorinformation includes the dynamic of the autonomous vehicle, a roadgeometry, a target vehicle, and a vehicle dynamic of the target vehicle.13. The method of claim 12, wherein the behavior of the vehicle operatorincludes at least one of a glance of the eyes, facial expression, bodygesture, and body posture.
 14. The method of claim 11, wherein analyzingthe behavior of the vehicle operator includes searching for a historicalvehicle operator's response to a similar historical upcoming trafficpattern.
 15. The method of claim 12, wherein initiating the change inthe dynamic of the autonomous vehicle includes increasing a distancebetween the autonomous vehicle and an adjacent vehicle.
 16. A method ofmanaging a warning priority to an operator of a vehicle, comprising:gathering exterior information on a surrounding about the vehicle;gathering interior information on the operator of the vehicle; analyzingthe exterior information to determine an upcoming traffic pattern;analyzing the interior information to determine an operator behavior inresponse to the upcoming traffic pattern; predicting an operator actionbased on the determined operator behavior in a response to thedetermined upcoming traffic pattern; and prioritizing a warning based onthe predicted operator action.
 17. The method of claim 16, whereinpredicting an operator action based on the determined operator behaviorin a response to the determined upcoming traffic pattern, comprises:searching a historical data base for a historical traffic patternsimilar to the determined upcoming traffic pattern and a historicaloperator behavior and action in response to the historical trafficpattern.
 18. The method of claim 17, wherein prioritizing the warningbased on the predicted operator action, comprises: not issuing an alertwhen the predicted operator action is taking-over the vehicle.
 19. Themethod of claim 17, wherein prioritizing the warning based on thepredicted operator action, comprises: issuing an alert when thepredicted operator action is not taking-over the vehicle.
 20. The methodof claim 16, wherein the behavior of the vehicle operator may bedetermined based on one or more of: eye glances, facial expressions,body gestures, and other relevant biometric factors exhibited by thevehicle operator.